Bacteroides fluxus (CCUG 60912, DSM 22534, JCM 16101)

Name: Bacteroides fluxus (CCUG 60912, DSM 22534, JCM 16101)
Creator: BacDive
License: http://creativecommons.org/licenses/by/4.0/

BacDive ID 1629

Type strain

Culture col. no. CCUG 60912 DSM 22534 JCM 16101 YIT 12057 KCTC 15129

NCBI tax ID(s) 763034 626930

Special Collections DSMZ CCUG JCM KCTC Literature strains

History <- Y. Watanabe, Yakult Central Inst. Microbiol. Res., Tokyo, Japan <- F. Nagai and M. Morotomi Y. Watanabe A159 (=YIT 12057).

Links

Bacteroides fluxus CCUG 60912 is an anaerobe, mesophilic, Gram-negative prokaryote that was isolated from human faeces .

Gram-negative rod-shaped anaerobe mesophilic genome sequence 16S sequence

Name and taxonomic classification

SI-ID 403507

JCM 16101,DSM 22534,CCUG 60912T

@ref 20215

Last LPSN update 2026-02-09 11:18:09

Domain Bacteria

Phylum Bacteroidota

Class Bacteroidia

Order Bacteroidales

Family Bacteroidaceae

Genus Bacteroides

Species Bacteroides fluxus

Full scientific name Bacteroides fluxus Watanabe et al. 2010

Morphology

Cell morphology

@ref	Gram stain	Cell length	Cell width	Cell shape	Confidence
29531	negative	1-2.5 µm	1-1.4 µm	rod-shaped
125439	negative				99.7
125438	negative				93.258

Pigmentation

29531

Productionyes

Culture and growth conditions

Culture medium

@ref	Name	Growth	Medium link	Composition
16382	CHOPPED MEAT MEDIUM (DSMZ Medium 78)		Medium recipe at MediaDive	Name: CHOPPED MEAT MEDIUM (DSMZ Medium 78) Composition: Ground beef 500.0 g/l Casitone 30.0 g/l Agar 15.0 g/l Ethanol 9.5 g/l (optional) K2HPO4 5.0 g/l Yeast extract 5.0 g/l L-Cysteine HCl 0.5 g/l Haemin 0.005 g/l (optional) Resazurin 0.001 g/l Vitamin K3 0.0005 g/l (optional) Vitamin K1 (optional) NaOH (optional) Distilled water

Culture temp

@ref	Growth	Type	Temperature (°C)	Range
16382	positive	growth	37	mesophilic
29531	positive	growth	37	mesophilic
29531	positive	optimum	37	mesophilic
62495	positive	growth	37	mesophilic
67770	positive	growth	37	mesophilic

Culture pH

@ref	Ability	Type	PH
29531	positive	optimum	7
29531	positive	growth	7

Physiology and metabolism

Oxygen tolerance

@ref	Oxygen tolerance	Confidence
16382	anaerobe
29531	anaerobe
62495	anaerobe
125439	obligate anaerobe	98.9

Spore formation

@ref	Spore formation	Confidence
29531
125439		99.8
125438		92.927

Observation

@ref	Observation
29531	aggregates in chains
67770	quinones: MK-10, MK-11

Metabolite utilization

@ref	Chebi-ID	Metabolite	Utilization activity	Kind of utilization tested
29531	22599 ChEBI	arabinose	+	carbon source
68380	29016 ChEBI	arginine	-	hydrolysis	from API rID32A
68367	17057 ChEBI	cellobiose	+	builds acid from	from API 20A
68367	17634 ChEBI	D-glucose	+	builds acid from	from API 20A
68367	16899 ChEBI	D-mannitol	-	builds acid from	from API 20A
68380	16024 ChEBI	D-mannose	+	fermentation	from API rID32A
68367	16024 ChEBI	D-mannose	+	builds acid from	from API 20A
68367	65327 ChEBI	D-xylose	+	builds acid from	from API 20A
68367	4853 ChEBI	esculin	+	hydrolysis	from API 20A
68367	5291 ChEBI	gelatin	-	hydrolysis	from API 20A
29531	17754 ChEBI	glycerol	+	carbon source
68367	17754 ChEBI	glycerol	-	builds acid from	from API 20A
68367	30849 ChEBI	L-arabinose	-	builds acid from	from API 20A
68380	29985 ChEBI	L-glutamate	-	degradation	from API rID32A
68367	62345 ChEBI	L-rhamnose	+	builds acid from	from API 20A
68367	17716 ChEBI	lactose	+	builds acid from	from API 20A
68367	17306 ChEBI	maltose	+	builds acid from	from API 20A
29531	29864 ChEBI	mannitol	+	carbon source
68367	6731 ChEBI	melezitose	-	builds acid from	from API 20A
68380	17632 ChEBI	nitrate	-	reduction	from API rID32A
68367	16634 ChEBI	raffinose	-	builds acid from	from API 20A
29531	26546 ChEBI	rhamnose	+	carbon source
29531	17814 ChEBI	salicin	+	carbon source
68367	17814 ChEBI	salicin	+	builds acid from	from API 20A
29531	30911 ChEBI	sorbitol	+	carbon source
68367	30911 ChEBI	sorbitol	-	builds acid from	from API 20A
68367	17992 ChEBI	sucrose	-	builds acid from	from API 20A
29531	27082 ChEBI	trehalose	+	carbon source
68367	27082 ChEBI	trehalose	-	builds acid from	from API 20A
68380	27897 ChEBI	tryptophan	+	energy source	from API rID32A
68367	27897 ChEBI	tryptophan	+	energy source	from API 20A
68380	16199 ChEBI	urea	-	hydrolysis	from API rID32A
68367	16199 ChEBI	urea	-	hydrolysis	from API 20A

Metabolite production

@ref	Chebi-ID	Metabolite	Production
68380	35581 ChEBI	indole		from API rID32A
68367	35581 ChEBI	indole		from API 20A

Metabolite tests

@ref	Chebi-ID	Metabolite	Indole test
68380	35581 ChEBI	indole	+	from API rID32A
68367	35581 ChEBI	indole	+	from API 20A

Enzymes

@ref	Value	Activity	Ec
68380	alanine arylamidase	+	3.4.11.2	from API rID32A
68380	alkaline phosphatase	+	3.1.3.1	from API rID32A
68380	alpha-arabinosidase	-	3.2.1.55	from API rID32A
68380	alpha-fucosidase	+	3.2.1.51	from API rID32A
29531	alpha-galactosidase	+	3.2.1.22
68380	alpha-galactosidase	+	3.2.1.22	from API rID32A
68380	alpha-glucosidase	+	3.2.1.20	from API rID32A
68380	arginine dihydrolase	-	3.5.3.6	from API rID32A
68380	beta-galactosidase	+	3.2.1.23	from API rID32A
68380	beta-Galactosidase 6-phosphate	-		from API rID32A
68380	beta-glucosidase	+	3.2.1.21	from API rID32A
68367	beta-glucosidase	+	3.2.1.21	from API 20A
68380	beta-glucuronidase	+	3.2.1.31	from API rID32A
68367	catalase	-	1.11.1.6	from API 20A
68367	gelatinase	-		from API 20A
68380	glutamate decarboxylase	-	4.1.1.15	from API rID32A
68380	glutamyl-glutamate arylamidase	+		from API rID32A
68380	glycin arylamidase	-		from API rID32A
68380	histidine arylamidase	-		from API rID32A
68380	L-arginine arylamidase	-		from API rID32A
68380	leucine arylamidase	-	3.4.11.1	from API rID32A
68380	leucyl glycin arylamidase	+	3.4.11.1	from API rID32A
68380	N-acetyl-beta-glucosaminidase	+	3.2.1.52	from API rID32A
68380	phenylalanine arylamidase	-		from API rID32A
68380	proline-arylamidase	-	3.4.11.5	from API rID32A
68380	pyrrolidonyl arylamidase	-	3.4.19.3	from API rID32A
68380	serine arylamidase	-		from API rID32A
68380	tryptophan deaminase	+	4.1.99.1	from API rID32A
68380	tyrosine arylamidase	-		from API rID32A
68380	urease	-	3.5.1.5	from API rID32A
68367	urease	-	3.5.1.5	from API 20A

Pathway annotation

Computationally determined by

@ref	pathway	enzyme coverage	annotated reactions
66794	C4 and CAM-carbon fixation	100	8 of 8
66794	CMP-KDO biosynthesis	100	4 of 4
66794	gluconeogenesis	100	8 of 8
66794	cis-vaccenate biosynthesis	100	2 of 2
66794	palmitate biosynthesis	100	22 of 22
66794	glycogen metabolism	100	5 of 5
66794	starch degradation	100	10 of 10
66794	biotin biosynthesis	100	4 of 4
66794	L-lactaldehyde degradation	100	3 of 3
66794	formaldehyde oxidation	100	3 of 3
66794	coenzyme A metabolism	100	4 of 4
66794	suberin monomers biosynthesis	100	2 of 2
66794	anapleurotic synthesis of oxalacetate	100	1 of 1
66794	UDP-GlcNAc biosynthesis	100	3 of 3
66794	CDP-diacylglycerol biosynthesis	100	2 of 2
66794	sulfopterin metabolism	100	4 of 4
66794	folate polyglutamylation	100	1 of 1
66794	chorismate metabolism	88.89	8 of 9
66794	aspartate and asparagine metabolism	88.89	8 of 9
66794	NAD metabolism	88.89	16 of 18
66794	lipid A biosynthesis	88.89	8 of 9
66794	tetrahydrofolate metabolism	85.71	12 of 14
66794	propanol degradation	85.71	6 of 7
66794	vitamin B1 metabolism	84.62	11 of 13
66794	vitamin B6 metabolism	81.82	9 of 11
66794	cellulose degradation	80	4 of 5
66794	lipoate biosynthesis	80	4 of 5
66794	metabolism of amino sugars and derivatives	80	4 of 5
66794	threonine metabolism	80	8 of 10
66794	vitamin K metabolism	80	4 of 5
66794	methylglyoxal degradation	80	4 of 5
66794	photosynthesis	78.57	11 of 14
66794	valine metabolism	77.78	7 of 9
66794	d-mannose degradation	77.78	7 of 9
66794	glycolysis	76.47	13 of 17
66794	glycogen biosynthesis	75	3 of 4
66794	ppGpp biosynthesis	75	3 of 4
66794	isoleucine metabolism	75	6 of 8
66794	acetate fermentation	75	3 of 4
66794	peptidoglycan biosynthesis	73.33	11 of 15
66794	flavin biosynthesis	73.33	11 of 15
66794	alanine metabolism	72.41	21 of 29
66794	cardiolipin biosynthesis	71.43	5 of 7
66794	citric acid cycle	71.43	10 of 14
66794	Entner Doudoroff pathway	70	7 of 10
66794	propionate fermentation	70	7 of 10
66794	phenylalanine metabolism	69.23	9 of 13
66794	histidine metabolism	68.97	20 of 29
66794	pyrimidine metabolism	68.89	31 of 45
66794	purine metabolism	68.09	64 of 94
66794	octane oxidation	66.67	2 of 3
66794	serine metabolism	66.67	6 of 9
66794	acetoin degradation	66.67	2 of 3
66794	vitamin B12 metabolism	64.71	22 of 34
66794	glutamate and glutamine metabolism	64.29	18 of 28
66794	pentose phosphate pathway	63.64	7 of 11
66794	d-xylose degradation	63.64	7 of 11
66794	degradation of sugar alcohols	62.5	10 of 16
66794	6-hydroxymethyl-dihydropterin diphosphate biosynthesis	62.5	5 of 8
66794	leucine metabolism	61.54	8 of 13
66794	degradation of hexoses	61.11	11 of 18
66794	hydrogen production	60	3 of 5
66794	O-antigen biosynthesis	60	3 of 5
66794	isoprenoid biosynthesis	57.69	15 of 26
66794	ubiquinone biosynthesis	57.14	4 of 7
66794	proline metabolism	54.55	6 of 11
66794	methionine metabolism	53.85	14 of 26
66794	sulfate reduction	53.85	7 of 13
66794	non-pathway related	52.63	20 of 38
66794	ethanol fermentation	50	1 of 2
66794	adipate degradation	50	1 of 2
66794	ketogluconate metabolism	50	4 of 8
66794	dTDPLrhamnose biosynthesis	50	4 of 8
66794	degradation of pentoses	50	14 of 28
66794	kanosamine biosynthesis II	50	1 of 2
66794	aminopropanol phosphate biosynthesis	50	1 of 2
66794	phenylmercury acetate degradation	50	1 of 2
66794	pantothenate biosynthesis	50	3 of 6
66794	butanoate fermentation	50	2 of 4
66794	lipid metabolism	48.39	15 of 31
66794	degradation of sugar acids	48	12 of 25
66794	arginine metabolism	45.83	11 of 24
66794	lysine metabolism	45.24	19 of 42
66794	tryptophan metabolism	44.74	17 of 38
66794	CO2 fixation in Crenarchaeota	44.44	4 of 9
66794	cysteine metabolism	44.44	8 of 18
66794	tyrosine metabolism	42.86	6 of 14
66794	reductive acetyl coenzyme A pathway	42.86	3 of 7
66794	oxidative phosphorylation	41.76	38 of 91
66794	degradation of aromatic, nitrogen containing compounds	41.67	5 of 12
66794	myo-inositol biosynthesis	40	4 of 10
66794	glycine metabolism	40	4 of 10
66794	phenylacetate degradation (aerobic)	40	2 of 5
66794	arachidonate biosynthesis	40	2 of 5
66794	bacilysin biosynthesis	40	2 of 5
66794	phenylpropanoid biosynthesis	38.46	5 of 13
66794	phosphatidylethanolamine bioynthesis	38.46	5 of 13
66794	ascorbate metabolism	36.36	8 of 22
66794	heme metabolism	35.71	5 of 14
66794	acetyl CoA biosynthesis	33.33	1 of 3
66794	sphingosine metabolism	33.33	2 of 6
66794	molybdenum cofactor biosynthesis	33.33	3 of 9
66794	selenocysteine biosynthesis	33.33	2 of 6
66794	cyanate degradation	33.33	1 of 3
66794	enterobactin biosynthesis	33.33	1 of 3
66794	glycolate and glyoxylate degradation	33.33	2 of 6
66794	IAA biosynthesis	33.33	1 of 3
66794	urea cycle	30.77	4 of 13
66794	coenzyme M biosynthesis	30	3 of 10
66794	bile acid biosynthesis, neutral pathway	29.41	5 of 17
66794	arachidonic acid metabolism	27.78	5 of 18
66794	metabolism of disaccharids	27.27	3 of 11
66794	polyamine pathway	26.09	6 of 23
66794	alginate biosynthesis	25	1 of 4
66794	toluene degradation	25	1 of 4
66794	cyclohexanol degradation	25	1 of 4
66794	lactate fermentation	25	1 of 4
66794	carnitine metabolism	25	2 of 8
66794	nitrate assimilation	22.22	2 of 9
66794	glutathione metabolism	21.43	3 of 14

API 20A

@ref	IND	URE	GLU	MAN	LAC	SAC	MAL	SAL	XYL	ARA	GEL	ESC	GLY	CEL	MNE	MLZ	RAF	SOR	RHA	TRE	CAT	Spores presentSPOR	GramGRAM	Morphology coccus="+" rod="-"COCC
16382	+	-	+	-	+	-	+	+	+	-	-	+	-	+	+	-	-	-	+	-	-	not determinedn.d.	not determinedn.d.	not determinedn.d.

API rID32A

@ref	URE	ADH (Arg)	alpha GAL	beta GAL	beta-Galactosidase 6-phosphatebeta GP	alpha GLU	beta GLU	alpha ARA	beta GUR	beta-N-Acetyl-beta-glucosaminidasebeta NAG	MNE	RAF	GDC	alpha FUC	Reduction of nitrateNIT	IND	PAL	L-arginine arylamidaseArgA	ProA	LGA	Phenylalanine arylamidasePheA	Leucine arylamidaseLeuA	PyrA	Tyrosine arylamidaseTyrA	Alanine arylamidaseAlaA	Glycin arylamidaseGlyA	Histidine arylamidaseHisA	Glutamyl-glutamate arylamidaseGGA	Serine arylamidaseSerA
16382	-	-	+	+	-	+	+	-	+	+	+	+/-	-	+	-	+	+	-	-	+	-	-	-	-	+	-	-	+	-

Isolation, sampling and environmental information

Isolation source categories

Cat1	Cat2	Cat3
#Host	#Human	#Male
#Host Body Product	#Gastrointestinal tract	#Feces (Stool)

Isolation

@ref	Sample type	Geographic location	Country	Country ISO 3 Code	Continent	Sampling date
16382	human faeces (healthy Japanese adult male)	Tokyo	Japan	JPN	Asia
62495	Human feces,healthy adult man		Japan	JPN	Asia	2009
67770	Human feces

Taxonmaps

69479

Global distribution of 16S sequence AB547642 (>99% sequence identity) for Bacteroides fluxus subclade from Microbeatlas

Aquatic Samples	1044
Soil Samples	324
Animal Samples	85995
Plant Samples	215
Total Samples	87578

Interaction and safety

Risk assessment

@ref	Biosafety level	Biosafety level comment
16382	1	Risk group (German classification) According to TRBA 466

Sequence information

Genome sequences

@ref	Description	Assembly level	INSDC accession	BV-BRC accession	IMG accession	NCBI tax ID	Score
67770	ASM19563v1 assembly for Bacteroides fluxus YIT 12057	scaffold	GCA_000195635	763034.3	651324011	763034	55.38

16S sequences

@ref	Description	Accession	Length	Database	NCBI tax ID
20218	Bacteroides fluxus gene for 16S ribosomal RNA, partial sequence, strain: JCM 16101	AB547642	1486		763034
16382	Bacteroides fluxus gene for 16S rRNA, partial sequence, strain: YIT 12057	AB490802	1477		763034

GC content

@ref	GC-content (mol%)	Method
16382	45.2	high performance liquid chromatography (HPLC)
29531	45.2

Genome-based predictions

Predictions

Predictions from Deep Learning for Genome Sequence Data. R package version 0.3.1 based on: Bacteroides fluxus YIT 12057
NCBI: GCA_000195635

@ref	Trait	Model	Prediction	Confidence in %	In training data
125439	spore_formation	BacteriaNetⓘ	no	99.80	no
125439	motility	BacteriaNetⓘ	no	65.70	no
125439	gram_stain	BacteriaNetⓘ	negative	99.70	no
125439	oxygen_tolerance	BacteriaNetⓘ	obligate anaerobe	98.90	no

Predictions from bacterial phenotypic traits through improved machine learning using high-quality, curated datasets based on: Bacteroides fluxus YIT 12057
PATRIC: 763034.3

@ref	Trait	Model	Prediction	Confidence in %	In training data
125438	gram-positive	gram-positiveⓘ	no	93.26	yes
125438	anaerobic	anaerobicⓘ	yes	78.03	no
125438	aerobic	aerobicⓘ	no	89.00	yes
125438	spore-forming	spore-formingⓘ	no	92.93	no
125438	thermophilic	thermophileⓘ	no	96.48	yes
125438	flagellated	motile2+ⓘ	no	88.25	yes

Literature

Topic	Title	Authors	Journal	DOI	Year
Phylogeny	Alterations in the gut microbiota associated with HIV-1 infection.	Lozupone CA, Li M, Campbell TB, Flores SC, Linderman D, Gebert MJ, Knight R, Fontenot AP, Palmer BE.	Cell Host Microbe	10.1016/j.chom.2013.08.006	2013
	Age-related sarcopenia and altered gut microbiota: A systematic review.	Wang M, Ren F, Zhou Y, He Y, Du T, Tan Y.	Microb Pathog	10.1016/j.micpath.2024.106850	2024
	Microbial diversity in drug-naïve Parkinson's disease patients.	Papic E, Racki V, Hero M, Zimani AN, Cizek Sajko M, Rozmaric G, Starcevic Cizmarevic N, Ostojic S, Kapovic M, Hauser G, Maver A, Peterlin B, Kovanda A, Vuletic V.	PLoS One	10.1371/journal.pone.0328761	2025
	Metagenomic sequencing revealed the regulative effect of Danshen and Honghua herb pair on the gut microbiota in rats with myocardial ischemia injury.	Du SB, Zhou HH, Xue ZP, Gao S, Li J, Meng Y, Zhao YJ, Wang PF, Li N, Bai JX, Bai JQ, Wang XP.	FEMS Microbiol Lett	10.1093/femsle/fnad133	2024
Phenotype	MetOrigin 2.0: Advancing the discovery of microbial metabolites and their origins.	Yu G, Xu C, Wang X, Ju F, Fu J, Ni Y.	Imeta	10.1002/imt2.246	2024
Pathogenicity	First case of abdominal infection caused by bacteroides fluxus.	Cobo F, Perez-Carrasco V, Gomez-Vicente E, Martin-Hita L, Garcia-Salcedo JA, Navarro-Mari JM.	Anaerobe	10.1016/j.anaerobe.2021.102363	2021
	The role of the gut microbiota in chemotherapy response, efficacy and toxicity: a systematic review.	Bohm D, Russ E, Guchelaar HJ, Ziemons J, Penders J, Smidt ML, van Best N, Deenen MJ.	NPJ Precis Oncol	10.1038/s41698-025-01034-0	2025
Genetics	Metagenomic Analysis Reveals the Characteristics of Cecal Microbiota in Chickens with Different Levels of Resistance During Recovery from Eimeria tenella Infection.	Tang J, Dong L, Tang M, Arif A, Zhang H, Zhang G, Zhang T, Xie K, Su S, Zhao Z, Dai G.	Animals (Basel)	10.3390/ani15101500	2025
	Gut microbiota and physical activity level: characterization from sedentary to soccer players.	Petri C, Mascherini G, Izzicupo P, Rosati D, Cerboneschi M, Smeazzetto S, Arrones LS.	Biol Sport	10.5114/biolsport.2024.134759	2024
	Comparison of the gut microbiota in older people with and without sarcopenia: a systematic review and meta-analysis.	Ren Y, He X, Wang L, Chen N.	Front Cell Infect Microbiol	10.3389/fcimb.2025.1480293	2025
	High interindividual variability of indoxyl sulfate production identified by an oral tryptophan challenge test.	Lin TY, Wu WK, Hung SC.	NPJ Biofilms Microbiomes	10.1038/s41522-025-00651-8	2025
	Bifidobacterium as a Potential Biomarker of Sarcopenia in Elderly Women.	Wang Z, Xu X, Deji Y, Gao S, Wu C, Song Q, Shi Z, Xiang X, Zang J, Su J.	Nutrients	10.3390/nu15051266	2023
	Genes for laminarin degradation are dispersed in the genomes of particle-associated Maribacter species.	Kalenborn S, Zuhlke D, Reintjes G, Riedel K, Amann RI, Harder J.	Front Microbiol	10.3389/fmicb.2024.1393588	2024
	Comparative analysis of gut microbiota between common (Macaca fascicularis fascicularis) and Burmese (M. f. aurea) long-tailed macaques in different habitats.	Muhammad R, Klomkliew P, Chanchaem P, Sawaswong V, Kaikaew T, Payungporn S, Malaivijitnond S.	Sci Rep	10.1038/s41598-023-42220-z	2023
Genetics	Population-based metagenomics analysis reveals altered gut microbiome in sarcopenia: data from the Xiangya Sarcopenia Study.	Wang Y, Zhang Y, Lane NE, Wu J, Yang T, Li J, He H, Wei J, Zeng C, Lei G.	J Cachexia Sarcopenia Muscle	10.1002/jcsm.13037	2022
Phylogeny	Identifying fecal microbiota signatures of colorectal cancer in a Vietnamese cohort.	Nhung PTT, Le HTT, Nguyen QH, Huyen DT, Quyen DV, Song LH, Van Thuan T, Tran TTT.	Front Microbiol	10.3389/fmicb.2024.1388740	2024
Genetics	Effects of Short-Chain Fatty Acid Modulation on Potentially Diarrhea-Causing Pathogens in Yaks Through Metagenomic Sequencing.	Li K, Zeng Z, Liu J, Pei L, Wang Y, Li A, Kulyar MF, Shahzad M, Mehmood K, Li J, Qi D.	Front Cell Infect Microbiol	10.3389/fcimb.2022.805481	2022
	Microbial metabolite ammonia disrupts TGF-beta signaling to promote colon cancer.	Bhowmick K, Yang X, Mohammad T, Xiang X, Molmenti CL, Mishra B, Dasarathy S, Krainer AR, Hassan MI, Crandall KA, Mishra L.	J Biol Chem	10.1016/j.jbc.2025.108559	2025
Phylogeny	Unraveling the distinctive gut microbiome of khulans (Equus hemionus hemionus) in comparison to their drinking water and closely related equids.	Jarquin-Diaz VH, Dayaram A, Soilemetzidou ES, Desvars-Larrive A, Bohner J, Buuveibaatar B, Kaczensky P, Walzer C, Greenwood AD, Lober U.	Sci Rep	10.1038/s41598-025-87216-z	2025
Genetics	A Comparison of the Cecal Microbiota between the Infection and Recovery Periods in Chickens with Different Susceptibilities to Eimeria tenella.	Tang J, Wang Q, Yu H, Dong L, Tang M, Arif A, Zhang G, Zhang T, Xie K, Su S, Zhao Z, Dai G.	Animals (Basel)	10.3390/ani14182709	2024
	Therapeutic Effect of Proteinase-Activated Receptor-1 Antagonist on Colitis-Associated Carcinogenesis.	Li X, Kurahara LH, Zhao Z, Zhao F, Ishikawa R, Ohmichi K, Li G, Yamashita T, Hashimoto T, Hirano M, Sun Z, Hirano K.	Cell Mol Gastroenterol Hepatol	10.1016/j.jcmgh.2024.04.001	2024
	Predicting Adverse Recanalization Therapy Outcomes in Acute Ischemic Stroke Patients Using Characteristic Gut Microbiota.	Chou PS, Hung WC, Yang IH, Kuo CM, Wu MN, Lin TC, Fong YO, Juan CH, Lai CL.	Microorganisms	10.3390/microorganisms11082016	2023
Pathogenicity	Intestinal flora characteristics of advanced non-small cell lung cancer in China and their role in chemotherapy based on metagenomics: A prospective exploratory cohort study.	Zhang M, Liu D, Zhou H, Liu X, Li X, Cheng Y, Gao B, Chen J.	Thorac Cancer	10.1111/1759-7714.14199	2021
Phylogeny	From gut to liver: unveiling the differences of intestinal microbiota in NAFL and NASH patients.	Huang F, Lyu B, Xie F, Li F, Xing Y, Han Z, Lai J, Ma J, Zou Y, Zeng H, Xu Z, Gao P, Luo Y, Bolund L, Tong G, Fengping X.	Front Microbiol	10.3389/fmicb.2024.1366744	2024
	Metagenomic and metabolomic analyses reveal the role of gut microbiome-associated metabolites in diarrhea calves.	Shi Z, Wang Y, Yan X, Ma X, Duan A, Hassan F-u, Wang W, Deng T.	mSystems	10.1128/msystems.00582-23	2023
	Prevotella copri alleviates sarcopenia via attenuating muscle mass loss and function decline.	Liu X, Wu J, Tang J, Xu Z, Zhou B, Liu Y, Hu F, Zhang G, Cheng R, Xia X, Chen Y, Wu H, Wang D, Yue J, Dong B, Fu J, Yu H, Dong B.	J Cachexia Sarcopenia Muscle	10.1002/jcsm.13313	2023
Enzymology	Structure and function of microbial alpha-l-fucosidases: a mini review.	Wu H, Owen CD, Juge N.	Essays Biochem	10.1042/ebc20220158	2023
Genetics	Bacterial community structure alterations within the colorectal cancer gut microbiome.	Loftus M, Hassouneh SA, Yooseph S.	BMC Microbiol	10.1186/s12866-021-02153-x	2021
	Influence of dietary n-3 long-chain fatty acids on microbial diversity and composition of sows' feces, colostrum, milk, and suckling piglets' feces.	Llaurado-Calero E, Climent E, Chenoll E, Ballester M, Badiola I, Lizardo R, Torrallardona D, Esteve-Garcia E, Tous N.	Front Microbiol	10.3389/fmicb.2022.982712	2022
Metabolism	Synergy between Cell Surface Glycosidases and Glycan-Binding Proteins Dictates the Utilization of Specific Beta(1,3)-Glucans by Human Gut Bacteroides.	Dejean G, Tamura K, Cabrera A, Jain N, Pudlo NA, Pereira G, Viborg AH, Van Petegem F, Martens EC, Brumer H.	mBio	10.1128/mbio.00095-20	2020
	A new Illumina MiSeq high-throughput sequencing-based method for evaluating the composition of the Bacteroides community in the intestine using the rpsD gene sequence.	Wang C, Feng S, Xiao Y, Pan M, Zhao J, Zhang H, Zhai Q, Chen W.	Microb Biotechnol	10.1111/1751-7915.13651	2021
	Bacterial associations in the healthy human gut microbiome across populations.	Loftus M, Hassouneh SA, Yooseph S.	Sci Rep	10.1038/s41598-021-82449-0	2021
	Gut microbiome is associated with the clinical response to anti-PD-1 based immunotherapy in hepatobiliary cancers.	Mao J, Wang D, Long J, Yang X, Lin J, Song Y, Xie F, Xun Z, Wang Y, Wang Y, Li Y, Sun H, Xue J, Song Y, Zuo B, Zhang J, Bian J, Zhang T, Yang X, Zhang L, Sang X, Zhao H.	J Immunother Cancer	10.1136/jitc-2021-003334	2021
Genetics	Characteristics and variation of fecal bacterial communities and functions in isolated systolic and diastolic hypertensive patients.	Wang P, Dong Y, Zuo K, Han C, Jiao J, Yang X, Li J.	BMC Microbiol	10.1186/s12866-021-02195-1	2021
	FiberGrowth Pipeline: A Framework Toward Predicting Fiber-Specific Growth From Human Gut Bacteroidetes Genomes.	Colnet B, Sieber CMK, Perraudeau F, Leclerc M.	Front Microbiol	10.3389/fmicb.2021.632567	2021
Metabolism	Intestinal microbial communities and Holdemanella isolated from HIV+/- men who have sex with men increase frequencies of lamina propria CCR5⁺ CD4⁺ T cells.	Yamada E, Martin CG, Moreno-Huizar N, Fouquier J, Neff CP, Coleman SL, Schneider JM, Huber J, Nusbacher NM, McCarter M, Campbell TB, Lozupone CA, Palmer BE.	Gut Microbes	10.1080/19490976.2021.1997292	2021
	Gut metagenomic characteristics of ADHD reveal low Bacteroides ovatus-associated host cognitive impairment.	Li Y, Sun H, Huang Y, Yin A, Zhang L, Han J, Lyu Y, Xu X, Zhai Y, Sun H, Wang P, Zhao J, Sun S, Dong H, Zhu F, Wang Q, Augusto Rohde L, Xie X, Sun X, Xiong L.	Gut Microbes	10.1080/19490976.2022.2125747	2022
	Metagenomic analysis of bacterial community structure and diversity of lignocellulolytic bacteria in Vietnamese native goat rumen.	Do TH, Dao TK, Nguyen KHV, Le NG, Nguyen TMP, Le TL, Phung TN, van Straalen NM, Roelofs D, Truong NH.	Asian-Australas J Anim Sci	10.5713/ajas.17.0174	2018
Metabolism	Quantifying fluorescent glycan uptake to elucidate strain-level variability in foraging behaviors of rumen bacteria.	Klassen L, Reintjes G, Tingley JP, Jones DR, Hehemann JH, Smith AD, Schwinghamer TD, Arnosti C, Jin L, Alexander TW, Amundsen C, Thomas D, Amann R, McAllister TA, Abbott DW.	Microbiome	10.1186/s40168-020-00975-x	2021
	Type VI secretion systems of human gut Bacteroidales segregate into three genetic architectures, two of which are contained on mobile genetic elements.	Coyne MJ, Roelofs KG, Comstock LE.	BMC Genomics	10.1186/s12864-016-2377-z	2016
Genetics	Noncontiguous finished genome sequence and description of Mediterranea massiliensis gen. nov., sp. nov., a new member of the Bacteroidaceae family isolated from human colon.	Ngom II, Mailhe M, Ricaboni D, Vitton V, Benezech A, Khelaifia S, Michelle C, Cadoret F, Armstrong N, Levasseur A, Raoult D, Million M.	New Microbes New Infect	10.1016/j.nmni.2017.11.009	2018
	Bacteroides muris sp. nov. isolated from the cecum of wild-derived house mice.	Fokt H, Unni R, Repnik U, Schmitz RA, Bramkamp M, Baines JF, Unterweger D.	Arch Microbiol	10.1007/s00203-022-03148-6	2022
Phylogeny	Bacteroides humanifaecis sp. nov., isolated from faeces of healthy Korean.	Kim HS, Kim JS, Suh MK, Eom MK, Lee JH, Park SH, Kang SW, Lee DH, Yoon H, Lee JH, Lee JS	Arch Microbiol	10.1007/s00203-022-02967-x	2022
Phylogeny	Bacteroides clarus sp. nov., Bacteroides fluxus sp. nov. and Bacteroides oleiciplenus sp. nov., isolated from human faeces.	Watanabe Y, Nagai F, Morotomi M, Sakon H, Tanaka R	Int J Syst Evol Microbiol	10.1099/ijs.0.015107-0	2009

References

#16382	Leibniz Institut DSMZ-Deutsche Sammlung von Mikroorganismen und Zellkulturen GmbH ; Curators of the DSMZ; DSM 22534
#20215	Parte, A.C., Sardà Carbasse, J., Meier-Kolthoff, J.P., Reimer, L.C. and Göker, M.: List of Prokaryotic names with Standing in Nomenclature (LPSN) moves to the DSMZ. IJSEM ( DOI 10.1099/ijsem.0.004332 )
#20218	Verslyppe, B., De Smet, W., De Baets, B., De Vos, P., Dawyndt P.: StrainInfo introduces electronic passports for microorganisms.. Syst Appl Microbiol. 37: 42 - 50 2014 ( DOI 10.1016/j.syapm.2013.11.002 , PubMed 24321274 )
#25928	IJSEM 1864 2010 ( DOI 10.1099/ijs.0.015107-0 , PubMed 19767355 )
#29531	Barberan A, Caceres Velazquez H, Jones S, Fierer N.: Hiding in Plain Sight: Mining Bacterial Species Records for Phenotypic Trait Information. mSphere 2: 2017 ( DOI 10.1128/mSphere.00237-17 , PubMed 28776041 ) - originally annotated from #25928
#62495	Culture Collection University of Gothenburg (CCUG) ; Curators of the CCUG; CCUG 60912
#66794	Antje Chang, Lisa Jeske, Sandra Ulbrich, Julia Hofmann, Julia Koblitz, Ida Schomburg, Meina Neumann-Schaal, Dieter Jahn, Dietmar Schomburg: BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res. 49: D498 - D508 2020 ( DOI 10.1093/nar/gkaa1025 , PubMed 33211880 )
#67770	Japan Collection of Microorganism (JCM) ; Curators of the JCM;
#68367	Automatically annotated from API 20A .
#68380	Automatically annotated from API rID32A .
#69479	João F Matias Rodrigues, Janko Tackmann,Gregor Rot, Thomas SB Schmidt, Lukas Malfertheiner, Mihai Danaila,Marija Dmitrijeva, Daniela Gaio, Nicolas Näpflin and Christian von Mering. University of Zurich.: MicrobeAtlas 1.0 beta .
#125438	Julia Koblitz, Lorenz Christian Reimer, Rüdiger Pukall, Jörg Overmann: Predicting bacterial phenotypic traits through improved machine learning using high-quality, curated datasets. 2024 ( DOI 10.1101/2024.08.12.607695 )
#125439	Philipp Münch, René Mreches, Martin Binder, Hüseyin Anil Gündüz, Xiao-Yin To, Alice McHardy: deepG: Deep Learning for Genome Sequence Data. R package version 0.3.1 .
#126262	A. Lissin, I. Schober, J. F. Witte, H. Lüken, A. Podstawka, J. Koblitz, B. Bunk, P. Dawyndt, P. Vandamme, P. de Vos, J. Overmann, L. C. Reimer: StrainInfo—the central database for linked microbial strain identifiers. ( DOI 10.1093/database/baaf059 )

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Bacteroides fluxus (CCUG 60912, DSM 22534, JCM 16101)

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